A novel simultaneous dynamic range compression and local contrast enhancement algorithm for digital video cameras

[[abstract]]This article addresses the problem of low dynamic range image enhancement for commercial digital cameras. A novel simultaneous dynamic range compression and local contrast enhancement algorithm (SDRCLCE) is presented to resolve this problem in a single-stage procedure. The proposed SDRCLCE algorithm is able to combine with many existent intensity transfer functions, which greatly increases the applicability of the proposed method. An adaptive intensity transfer function is also proposed to combine with SDRCLCE algorithm that provides the capability to adjustably control the level of overall lightness and contrast achieved at the enhanced output. Moreover, the proposed method is amenable to parallel processing implementation that allows us to improve the processing speed of SDRCLCE algorithm. Experimental results show that the performance of the proposed method outperforms three state-of-the-art methods in terms of dynamic range compression and local contrast enhancement.[[incitationindex]]SCI[[booktype]]電子

Enhancement and stylization of photographs

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 89-95).A photograph captured by a digital camera may be the final product for many casual photographers. However, for professional photographers, this photograph is only the beginning: experts often spend hours on enhancing and stylizing their photographs. These enhancements range from basic exposure and contrast adjustments to dramatic alterations. It is these enhancements - along with composition and timing - that distinguish the work of professionals and casual photographers. The goal of this thesis is to narrow the gap between casual and professional photographers. We aim to empower casual users with methods for making their photographs look better. Professional photographers could also benefit from our findings: our enhancement methods produce a better starting point for professional processing. We propose and evaluate three different methods for image enhancement and stylization. First method is based on photographic intuition and is fully automatic. The second method relies on expert's input for training; after the training this method can be used to automatically predict expert adjustments for previously unseen photographs. The third method uses a grammar-based representation to sample the space of image filter and relies on user input to select novel and interesting filters.by Vladimir Leonid Bychkovsky.Ph.D

Deep Bilateral Learning for Real-Time Image Enhancement

Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new slicing node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.Comment: 12 pages, 14 figures, Siggraph 201

Humanistic Computing: WearComp as a New Framework and Application for Intelligent Signal Processing

Humanistic computing is proposed as a new signal processing framework in which the processing apparatus is inextricably intertwined with the natural capabilities of our human body and mind. Rather than trying to emulate human intelligence, humanistic computing recognizes that the human brain is perhaps the best neural network of its kind, and that there are many new signal processing applications (within the domain of personal technologies) that can make use of this excellent but often overlooked processor. The emphasis of this paper is on personal imaging applications of humanistic computing, to take a first step toward an intelligent wearable camera system that can allow us to effortlessly capture our day-to-day experiences, help us remember and see better, provide us with personal safety through crime reduction, and facilitate new forms of communication through collective connected humanistic computing. The author’s wearable signal processing hardware, which began as a cumbersome backpackbased photographic apparatus of the 1970’s and evolved into a clothing-based apparatus in the early 1980’s, currently provides the computational power of a UNIX workstation concealed within ordinary-looking eyeglasses and clothing. Thus it may be worn continuously during all facets of ordinary day-to-day living, so that, through long-term adaptation, it begins to function as a true extension of the mind and body

TESTING COLOR APPEARANCE MODELS IN COMPLEX SCENE

The sensation of sight is our primary mechanism to perceive the world around us. However it is not yet perfectly clear how the human visual system works. The images of the world are formed on the retina, captured by sensors and converted in signals sent to the brain. Here the signals are processed and somehow interpreted, thus we are able to see. A lot of information, hypothesis, hints come from a field of the optical (or visual) illusions. These illusions have led many scientists and researchers to ask themselves why we are not able to interpret in a correct way some particular scenes. The word \u201cinterpret\u201d underlines the fact that the brain, and not only the eye, is involved in the process of vision. If our sight worked as a measurement tool, similar to a spectrophotometer, we would not perceive, for example, the simultaneous contrast phenomenon, in which a grey patch placed on a black background appears lighter than an identical coloured patch on a white background. So, why do we perceive the patches as different, while the light that reaches the eyes is the same? In the same way we would not be able to distinguish a white paper seen in a room lit with a red light from a red paper seen under a white light, however humans can do this. These phenomena are called colour appearance phenomena. Simulating the appearance is the objective of a range of computational models called colour appearance models. In this dissertation themes about colour appearance models are addressed. Specific experiments, performed by human observers, aim to evaluate and measure the appearance. Different algorithms are tested in order to compare the results of the computational model with the human sensations about colours. From these data, a new printing pipeline is developed, able to simulate the appearance of advertising billboard in different context

Computational Video Enhancement

During a video, each scene element is often imaged many times by the sensor. I propose that by combining information from each captured frame throughout the video it is possible to enhance the entire video. This concept is the basis of computational video enhancement. In this dissertation, the viability of computational video processing is explored in addition to presenting applications where this processing method can be leveraged. Spatio-temporal volumes are employed as a framework for efficient computational video processing, and I extend them by introducing sheared volumes. Shearing provides spatial frame warping for alignment between frames, allowing temporally-adjacent samples to be processed using traditional editing and filtering approaches. An efficient filter-graph framework is presented to support this processing along with a prototype video editing and manipulation tool utilizing that framework. To demonstrate the integration of samples from multiple frames, I introduce methods for improving poorly exposed low-light videos to achieve improved results. This integration is guided by a tone-mapping process to determine spatially-varying optimal exposures and an adaptive spatio-temporal filter to integrate the samples. Low-light video enhancement is also addressed in the multispectral domain by combining visible and infrared samples. This is facilitated by the use of a novel multispectral edge-preserving filter to enhance only the visible spectrum video. Finally, the temporal characteristics of videos are altered by a computational video resampling process. By resampling the video-rate footage, novel time-lapse sequences are found that optimize for user-specified characteristics. Each resulting shorter video is a more faithful summary of the original source than a traditional time-lapse video. Simultaneously, new synthetic exposures are generated to alter the output video's aliasing characteristics

Divide-and-conquer framework for image restoration and enhancement

Abstract(#br)We develop a novel divide-and-conquer framework for image restoration and enhancement based on their task-driven requirements, which takes advantage of visual importance differences of image contents (i.e., noise versus image, edge-based structures versus smoothing areas, high-frequency versus low-frequency components) and sparse prior differences of image contents for performance improvements. The proposed framework is efficient in implementation of decomposition-processing-integration. An observed image is first decomposed into different subspaces based on considering visual importance of different subspaces and exploiting their prior differences. Different models are separately established for image subspace restoration and enhancement, and existing image restoration and enhancement methods are utilized to deal with them effectively. Then a simple but effective fusion scheme with different weights is used to integrate the post-processed subspaces for the final reconstructed image. Final experimental results demonstrate that the proposed divide-and-conquer framework outperforms several restoration and enhancement algorithms in both subjective results and objective assessments. The performance improvements of image restoration and enhancement can be yielded by using the proposed divide-and-conquer strategy, which greatly benefits in terms of mixed Gaussian and salt-and-pepper noise removal, non-blind deconvolution, and image enhancement. In addition, our divide-and-conquer framework can be simply extensible to other restoration and enhancement algorithms, and can be a new way to promote their performances for image restoration and enhancement