Incident Light Frequency-based Image Defogging Algorithm

Considering the problem of color distortion caused by the defogging algorithm based on dark channel prior, an improved algorithm was proposed to calculate the transmittance of all channels respectively. First, incident light frequency's effect on the transmittance of various color channels was analyzed according to the Beer-Lambert's Law, from which a proportion among various channel transmittances was derived; afterwards, images were preprocessed by down-sampling to refine transmittance, and then the original size was restored to enhance the operational efficiency of the algorithm; finally, the transmittance of all color channels was acquired in accordance with the proportion, and then the corresponding transmittance was used for image restoration in each channel. The experimental results show that compared with the existing algorithm, this improved image defogging algorithm could make image colors more natural, solve the problem of slightly higher color saturation caused by the existing algorithm, and shorten the operation time by four to nine times

Photographic film image enhancement

A series of experiments were undertaken to assess the feasibility of defogging color film by the techniques of optical spatial filtering. A coherent optical processor was built using red, blue, and green laser light input and specially designed Fourier transformation lenses. An array of spatial filters was fabricated on black and white emulsion slides using the coherent optical processor. The technique was first applied to laboratory white light fogged film, and the results were successful. However, when the same technique was applied to some original Apollo X radiation fogged color negatives, the results showed no similar restoration. Examples of each experiment are presented and possible reasons for the lack of restoration in the Apollo films are discussed

Multi-modal Non-line-of-sight Passive Imaging

We consider the non-line-of-sight (NLOS) imaging of an object using the light reflected off a diffusive wall. The wall scatters incident light such that a lens is no longer useful to form an image. Instead, we exploit the 4D spatial coherence function to reconstruct a 2D projection of the obscured object. The approach is completely passive in the sense that no control over the light illuminating the object is assumed and is compatible with the partially coherent fields ubiquitous in both the indoor and outdoor environments. We formulate a multi-criteria convex optimization problem for reconstruction, which fuses the reflected field's intensity and spatial coherence information at different scales. Our formulation leverages established optics models of light propagation and scattering and exploits the sparsity common to many images in different bases. We also develop an algorithm based on the alternating direction method of multipliers to efficiently solve the convex program proposed. A means for analyzing the null space of the measurement matrices is provided as well as a means for weighting the contribution of individual measurements to the reconstruction. This paper holds promise to advance passive imaging in the challenging NLOS regimes in which the intensity does not necessarily retain distinguishable features and provides a framework for multi-modal information fusion for efficient scene reconstruction

A Review of Remote Sensing Image Dehazing.

Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated

BDPK: Bayesian Dehazing Using Prior Knowledge

IEEE Atmospheric scattering model (ASM) has been widely used in hazy image restoration. However, the recovered albedo might deviate from the real scene once the input hazy image cannot fully satisfy the model’s assumptions such as the homogeneous atmosphere and even illumination. In this paper, we break these limitations and redefine a more reliable atmospheric scattering model (RASM) that is extremely adaptable for various practical scenarios. Benefiting from RASM, a simple yet effective Bayesian dehazing algorithm (BDPK) is further proposed based on the prior knowledge. Our strategy is to convert the single image dehazing problem into a maximum a-posteriori probability (MAP) one that can be approximated as an optimization function using the existing priori constraints. To efficiently solve this optimization function, the alternating minimizing technique (AMT) is introduced, which enables us to directly restore the scene albedo. Experiments on a number of challenging images reveal the power of BDPK on removing haze and verify its superiority over several state-of-the-art techniques in terms of quality and efficiency

Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution

Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low light, intense glow, light scattering, and the presence of multicolored light sources. Existing nighttime dehazing methods often struggle with handling glow or low-light conditions, resulting in either excessively dark visuals or unsuppressed glow outputs. In this paper, we enhance the visibility from a single nighttime haze image by suppressing glow and enhancing low-light regions. To handle glow effects, our framework learns from the rendered glow pairs. Specifically, a light source aware network is proposed to detect light sources of night images, followed by the APSF (Angular Point Spread Function)-guided glow rendering. Our framework is then trained on the rendered images, resulting in glow suppression. Moreover, we utilize gradient-adaptive convolution, to capture edges and textures in hazy scenes. By leveraging extracted edges and textures, we enhance the contrast of the scene without losing important structural details. To boost low-light intensity, our network learns an attention map, then adjusted by gamma correction. This attention has high values on low-light regions and low values on haze and glow regions. Extensive evaluation on real nighttime haze images, demonstrates the effectiveness of our method. Our experiments demonstrate that our method achieves a PSNR of 30.38dB, outperforming state-of-the-art methods by 13$\%$ on GTA5 nighttime haze dataset. Our data and code is available at: \url{https://github.com/jinyeying/nighttime_dehaze}.Comment: Accepted to ACM'MM2023, https://github.com/jinyeying/nighttime_dehaz

Water drop-surface interactions as the basis for the design of anti-fogging surfaces : theory, practice, and applications trends

Glass- and polymer-based materials have become essential in the fabrication of a multitude of elements, including eyeglasses, automobile windshields, bathroom mirrors, greenhouses, and food packages, which unfortunately mist up under typical operating conditions. Far from being an innocuous phenomenon, the formation of minute water drops on the surface is detrimental to their optical properties (e.g., light-transmitting capability) and, in many cases, results in esthetical, hygienic, and safety concerns. In this context, it is therefore not surprising that research in the field of fog-resistant surfaces is gaining in popularity, particularly in recent years, in view of the growing number of studies focusing on this topic. This review addresses the most relevant advances released thus far on anti-fogging surfaces, with a particular focus on coating deposition, surface micro/nanostructuring, and surface functionalization. A brief explanation of how surfaces fog up and the main issues of interest linked to fogging phenomenon, including common problems, anti-fogging strategies, and wetting states are first presented. Anti-fogging mechanisms are then discussed in terms of the morphology of water drops, continuing with a description of the main fabrication techniques toward anti-fogging property. This review concludes with the current and the future perspectives on the utility of anti-fogging surfaces for several applications and some remaining challenges in this field

Development of An In Vivo Robotic Camera for Dexterous Manipulation and Clear Imaging

Minimally invasive surgeriy (MIS) techniques are becoming more popular as replacements for traditional open surgeries. These methods benefit patients with lowering blood loss and post-operative pain, reducing recovery period and hospital stay time, decreasing surgical area scarring and cosmetic issues, and lessening the treatment costs, hence greater patient satisfaction would be earned. Manipulating surgical instruments from outside of abdomen and performing surgery needs precise hand-eye coordination which is provided by insertable cameras. The traditional MIS insertable cameras suffer from port complexity and reduced manipulation dexterity, which leads to defection in Hand-eye coordination and surgical flow. Fully insertable robotic camera systems emerged as a promising solution in MIS. Implementing robotic camera systems faces multiple challenges in fixation, manipulation, orientation control, tool-tissue interaction, in vivo illumination and clear imaging.In this dissertation a novel actuation and control mechanism is developed and validated for an insertable laparoscopic camera. This design uses permanent magnets and coils as force/torque generators in an external control unit to manipulate an in vivo camera capsule. The motorless design of this capsule reduces the, wight, size and power consumption of the driven unit. In order to guarantee the smooth motion of the camera inside the abdominal cavity, an interaction force control method was proposed and validated.Optimizing the system\u27s design, through minimizing the control unit size and power consumption and extending maneuverability of insertable camera, was achieved by a novel transformable design, which uses a single permanent magnet in the control unit. The camera robot uses a permanent magnet as fixation and translation unit, and two embedded motor for tilt motion actuation, as well as illumination actuation. Transformable design provides superior imaging quality through an optimized illumination unit and a cleaning module. The illumination module uses freeform optical lenses to control light beams from the LEDs to achieve optimized illumination over surgical zone. The cleaning module prevents lens contamination through a pump actuated debris prevention system, while mechanically wipes the lens in case of contamination. The performance of transformable design and its modules have been assessed experimentally

Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods