Wavelet-Based Enhancement Technique for Visibility Improvement of Digital Images

Image enhancement techniques for visibility improvement of color digital images based on wavelet transform domain are investigated in this dissertation research. In this research, a novel, fast and robust wavelet-based dynamic range compression and local contrast enhancement (WDRC) algorithm to improve the visibility of digital images captured under non-uniform lighting conditions has been developed. A wavelet transform is mainly used for dimensionality reduction such that a dynamic range compression with local contrast enhancement algorithm is applied only to the approximation coefficients which are obtained by low-pass filtering and down-sampling the original intensity image. The normalized approximation coefficients are transformed using a hyperbolic sine curve and the contrast enhancement is realized by tuning the magnitude of the each coefficient with respect to surrounding coefficients. The transformed coefficients are then de-normalized to their original range. The detail coefficients are also modified to prevent edge deformation. The inverse wavelet transform is carried out resulting in a lower dynamic range and contrast enhanced intensity image. A color restoration process based on the relationship between spectral bands and the luminance of the original image is applied to convert the enhanced intensity image back to a color image. Although the colors of the enhanced images produced by the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce color constant results for some pathological scenes that have very strong spectral characteristics in a single band. The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for tackling the color constancy problem. The illuminant is modeled having an effect on the image histogram as a linear shift and adjust the image histogram to discount the illuminant. The WDRC algorithm is then applied with a slight modification, i.e. instead of using a linear color restoration, a non-linear color restoration process employing the spectral context relationships of the original image is applied. The proposed technique solves the color constancy issue and the overall enhancement algorithm provides attractive results improving visibility even for scenes with near-zero visibility conditions. In this research, a new wavelet-based image interpolation technique that can be used for improving the visibility of tiny features in an image is presented. In wavelet domain interpolation techniques, the input image is usually treated as the low-pass filtered subbands of an unknown wavelet-transformed high-resolution (HR) image, and then the unknown high-resolution image is produced by estimating the wavelet coefficients of the high-pass filtered subbands. The same approach is used to obtain an initial estimate of the high-resolution image by zero filling the high-pass filtered subbands. Detail coefficients are estimated via feeding this initial estimate to an undecimated wavelet transform (UWT). Taking an inverse transform after replacing the approximation coefficients of the UWT with initially estimated HR image, results in the final interpolated image. Experimental results of the proposed algorithms proved their superiority over the state-of-the-art enhancement and interpolation techniques

Real-World Image Restoration Using Degradation Adaptive Transformer-Based Adversarial Network

Most existing learning-based image restoration methods heavily rely on paired degraded/non-degraded training datasets that are based on simplistic handcrafted degradation assumptions. These assumptions often involve a limited set of degradations, such as Gaussian blurs, noises, and bicubic downsampling. However, when these methods are applied to real-world images, there is a significant decrease in performance due to the discrepancy between synthetic and realistic degradation. Additionally, they lack the flexibility to adapt to unknown degradations in practical scenarios, which limits their generalizability to complex and unconstrained scenes. To address the absence of image pairs, recent studies have proposed Generative Adversarial Network (GAN)-based unpaired methods. Nevertheless, unpaired learning models based on convolution operations encounter challenges in capturing long-range pixel dependencies in real-world images. This limitation stems from their reliance on convolution operations, which offer local connectivity and translation equivariance but struggle to capture global dependencies due to their limited receptive field. To address these challenges, this dissertation proposed an innovative unpaired image restoration basic model along with an advanced model. The proposed basic model is the DA-CycleGAN model, which is based on the CycleGAN [1] neural network and specifically designed for blind real-world Single Image Super-Resolution (SISR). The DA-CycleGAN incorporates a degradation adaptive (DA) module to learn various real-world degradations (such as noise and blur patterns) in an unpaired manner, enabling strong flexible adaptation. Additionally, an advanced model called Trans-CycleGAN was designed, which integrated the Transformer architecture into CycleGAN to leverage its global connectivity. This combination allowed for image-to-image translation using CycleGAN [1] while enabling the Transformer to model global connectivity across long-range pixels. Extensive experiments conducted on realistic images demonstrate the superior performance of the proposed method in solving real-world image restoration problems, resulting in clearer and finer details. Overall, this dissertation presents a novel unpaired image restoration basic model and an advanced model that effectively address the limitations of existing approaches. The proposed approach achieves significant advancements in handling real-world degradations and modeling long-range pixel dependencies, thereby offering substantial improvements in image restoration tasks. Index Terms— Cross-domain translation, generative adversarial network, image restoration, super-resolution, transformer, unpaired training

Scene Context Dependency of Pattern Constancy of Time Series Imagery

A fundamental element of future generic pattern recognition technology is the ability to extract similar patterns for the same scene despite wide ranging extraneous variables, including lighting, turbidity, sensor exposure variations, and signal noise. In the process of demonstrating pattern constancy of this kind for retinex/visual servo (RVS) image enhancement processing, we found that the pattern constancy performance depended somewhat on scene content. Most notably, the scene topography and, in particular, the scale and extent of the topography in an image, affects the pattern constancy the most. This paper will explore these effects in more depth and present experimental data from several time series tests. These results further quantify the impact of topography on pattern constancy. Despite this residual inconstancy, the results of overall pattern constancy testing support the idea that RVS image processing can be a universal front-end for generic visual pattern recognition. While the effects on pattern constancy were significant, the RVS processing still does achieve a high degree of pattern constancy over a wide spectrum of scene content diversity, and wide ranging extraneousness variations in lighting, turbidity, and sensor exposure

Ballistic Imaging of Transient Phenomena in Turbid Media

Ballistic imaging (BI) was developed as an optical diagnostic capable of ascertaining velocity and spatial information within dense sprays with relevance to liquid-fuel injection and combustion. This development includes a full model of light scattering within the complete imaging system, enabling the performance of the instrument to be examined, optimized and quantified. BI is a laser-based measurement for enhanced visualization of strong gradient disturbances within inhomogeneous highly scattering media. The technique is a specialized shadow-imaging method, closely related to schlieren and shadowgraph techniques, which focuses on eliminating stray and multiply-scattered source light from a line-of-sight integrated 2-D signal, detected in a forward-collection geometry. Experimental investigations of two turbulent sprays were conducted, and new spray behavior was observed over the course of both measurement campaigns: Bifurcation in a two-phase flow was observed for the first time, in a jet-in-crossflow, and overall flowrate effects were shown to influence the breakup characteristics in an effervescent spray. Analysis methods were developed to apply spatial correlation to BI results, enabling the determination of velocity vectors throughout a dense spray

OBJECT PERCEPTION IN UNDERWATER ENVIRONMENTS: A SURVEY ON SENSORS AND SENSING METHODOLOGIES

Underwater robots play a critical role in the marine industry. Object perception is the foundation for the automatic operations of submerged vehicles in dynamic aquatic environments. However, underwater perception encounters multiple environmental challenges, including rapid light attenuation, light refraction, or backscattering effect. These problems reduce the sensing devices’ signal-to-noise ratio (SNR), making underwater perception a complicated research topic. This paper describes the state-of-the-art sensing technologies and object perception techniques for underwater robots in different environmental conditions. Due to the current sensing modalities’ various constraints and characteristics, we divide the perception ranges into close-range, medium-range, and long-range. We survey and describe recent advances for each perception range and suggest some potential future research directions worthy of investigating in this field

Gated frequency-resolved optical imaging with an optical parametric amplifier for medical applications

Implementation of optical imagery in a diffuse inhomogeneous medium such as biological tissue requires an understanding of photon migration and multiple scattering processes which act to randomize pathlength and degrade image quality. The nature of transmitted light from soft tissue ranges from the quasi-coherent properties of the minimally scattered component to the random incoherent light of the diffuse component. Recent experimental approaches have emphasized dynamic path-sensitive imaging measurements with either ultrashort laser pulses (ballistic photons) or amplitude modulated laser light launched into tissue (photon density waves) to increase image resolution and transmissive penetration depth. Ballistic imaging seeks to compensate for these {open_quotes}fog-like{close_quotes} effects by temporally isolating the weak early-arriving image-bearing component from the diffusely scattered background using a subpicosecond optical gate superimposed on the transmitted photon time-of-flight distribution. The authors have developed a broadly wavelength tunable (470 nm -2.4 {mu}m), ultrashort amplifying optical gate for transillumination spectral imaging based on optical parametric amplification in a nonlinear crystal. The time-gated image amplification process exhibits low noise and high sensitivity, with gains greater than 104 achievable for low light levels. We report preliminary benchmark experiments in which this system was used to reconstruct, spectrally upcovert, and enhance near-infrared two-dimensional images with feature sizes of 65 {mu}m/mm{sup 2} in background optical attenuations exceeding 10{sup 12}. Phase images of test objects exhibiting both absorptive contrast and diffuse scatter were acquired using a self-referencing Shack-Hartmann wavefront sensor in combination with short-pulse quasi-ballistic gating. The sensor employed a lenslet array based on binary optics technology and was sensitive to optical path distortions approaching {lambda}/100