Dehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception

Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focused on evaluating the visual quality of dehazed images. In this paper, we propose a Reduced-Reference dehazed image quality evaluation approach based on Partial Discrepancy (RRPD) and then extend it to a No-Reference quality assessment metric with Blind Perception (NRBP). Specifically, inspired by the hierarchical characteristics of the human perceiving dehazed images, we introduce three groups of features: luminance discrimination, color appearance, and overall naturalness. In the proposed RRPD, the combined distance between a set of sender and receiver features is adopted to quantify the perceptually dehazed image quality. By integrating global and local channels from dehazed images, the RRPD is converted to NRBP which does not rely on any information from the references. Extensive experiment results on several dehazed image quality databases demonstrate that our proposed methods outperform state-of-the-art full-reference, reduced-reference, and no-reference quality assessment models. Furthermore, we show that the proposed dehazed image quality evaluation methods can be effectively applied to tune parameters for potential image dehazing algorithms

Super-resolution assessment and detection

Super Resolution (SR) techniques are powerful digital manipulation tools that have significantly impacted various industries due to their ability to enhance the resolution of lower quality images and videos. Yet, the real-world adaptation of SR models poses numerous challenges, which blind SR models aim to overcome by emulating complex real-world degradations. In this thesis, we investigate these SR techniques, with a particular focus on comparing the performance of blind models to their non-blind counterparts under various conditions. Despite recent progress, the proliferation of SR techniques raises concerns about their potential misuse. These methods can easily manipulate real digital content and create misrepresentations, which highlights the need for robust SR detection mechanisms. In our study, we analyze the limitations of current SR detection techniques and propose a new detection system that exhibits higher performance in discerning real and upscaled videos. Moreover, we conduct several experiments to gain insights into the strengths and weaknesses of the detection models, providing a better understanding of their behavior and limitations. Particularly, we target 4K videos, which are rapidly becoming the standard resolution in various fields such as streaming services, gaming, and content creation. As part of our research, we have created and utilized a unique dataset in 4K resolution, specifically designed to facilitate the investigation of SR techniques and their detection

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM