Capsule Endoscopy Multi-classification via Gated Attention and Wavelet Transformations

Abnormalities in the gastrointestinal tract significantly influence the patient's health and require a timely diagnosis for effective treatment. With such consideration, an effective automatic classification of these abnormalities from a video capsule endoscopy (VCE) frame is crucial for improvement in diagnostic workflows. The work presents the process of developing and evaluating a novel model designed to classify gastrointestinal anomalies from a VCE video frame. Integration of Omni Dimensional Gated Attention (OGA) mechanism and Wavelet transformation techniques into the model's architecture allowed the model to focus on the most critical areas in the endoscopy images, reducing noise and irrelevant features. This is particularly advantageous in capsule endoscopy, where images often contain a high degree of variability in texture and color. Wavelet transformations contributed by efficiently capturing spatial and frequency-domain information, improving feature extraction, especially for detecting subtle features from the VCE frames. Furthermore, the features extracted from the Stationary Wavelet Transform and Discrete Wavelet Transform are concatenated channel-wise to capture multiscale features, which are essential for detecting polyps, ulcerations, and bleeding. This approach improves classification accuracy on imbalanced capsule endoscopy datasets. The proposed model achieved 92.76% and 91.19% as training and validation accuracies respectively. At the same time, Training and Validation losses are 0.2057 and 0.2700. The proposed model achieved a Balanced Accuracy of 94.81%, AUC of 87.49%, F1-score of 91.11%, precision of 91.17%, recall of 91.19% and specificity of 98.44%. Additionally, the model's performance is benchmarked against two base models, VGG16 and ResNet50, demonstrating its enhanced ability to identify and classify a range of gastrointestinal abnormalities accurately.

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.

NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results

This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of four, from low-resolution (LR) inputs using prior information. The LR images originate from bicubic downsampling degradation. The aim of the challenge is to obtain designs/solutions with the most advanced SR performance, with no constraints on computational resources (e.g., model size and FLOPs) or training data. The track of this challenge assesses performance with the PSNR metric on the DIV2K testing dataset. The competition attracted 199 registrants, with 20 teams submitting valid entries. This collective endeavour not only pushes the boundaries of performance in single-image SR but also offers a comprehensive overview of current trends in this field.

SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution

Many applications such as forensics, surveillance, satellite imaging, medical imaging, etc., demand High-Resolution (HR) images. However, obtaining an HR image is not always possible due to the limitations of optical sensors and their costs. An alternative solution called Single Image Super-Resolution (SISR) is a software-driven approach that aims to take a Low-Resolution (LR) image and obtain the HR image. Most supervised SISR solutions use ground truth HR image as a target and do not include the information provided in the LR image, which could be valuable. In this work, we introduce Triplet Loss-based Generative Adversarial Network hereafter referred as SRTGAN for Image Super-Resolution problem on real-world degradation. We introduce a new triplet-based adversarial loss function that exploits the information provided in the LR image by using it as a negative sample. Allowing the patch-based discriminator with access to both HR and LR images optimizes to better differentiate between HR and LR images; hence, improving the adversary. Further, we propose to fuse the adversarial loss, content loss, perceptual loss, and quality loss to obtain Super-Resolution (SR) image with high perceptual fidelity. We validate the superior performance of the proposed method over the other existing methods on the RealSR dataset in terms of quantitative and qualitative metrics.

Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report

As the quality of mobile cameras starts to play a crucial role in modern smartphones, more and more attention is now being paid to ISP algorithms used to improve various perceptual aspects of mobile photos. In this Mobile AI challenge, the target was to develop an end-to-end deep learning-based image signal processing (ISP) pipeline that can replace classical hand-crafted ISPs and achieve nearly real-time performance on smartphone NPUs. For this, the participants were provided with a novel learned ISP dataset consisting of RAW-RGB image pairs captured with the Sony IMX586 Quad Bayer mobile sensor and a professional 102-megapixel medium format camera. The runtime of all models was evaluated on the MediaTek Dimensity 1000+ platform with a dedicated AI processing unit capable of accelerating both floating-point and quantized neural networks. The proposed solutions are fully compatible with the above NPU and are capable of processing Full HD photos under 60-100 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.