Comparing deep learning models for low-light natural scene image enhancement and their impact on object detection and classification: Overview, empirical …
Low-light image (LLI) enhancement is an important image processing task that aims at
improving the illumination of images taken under low-light conditions. Recently, a …
improving the illumination of images taken under low-light conditions. Recently, a …
From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms
Image denoising is a well explored topic in the field of image processing. In the past several
decades, the progress made in image denoising has benefited from the improved modeling …
decades, the progress made in image denoising has benefited from the improved modeling …
Image blind denoising with generative adversarial network based noise modeling
In this paper, we consider a typical image blind denoising problem, which is to remove
unknown noise from noisy images. As we all know, discriminative learning based methods …
unknown noise from noisy images. As we all know, discriminative learning based methods …
High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling
Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion
diagnosis, prognosis, and delineation. However, gradient power and hardware limitations …
diagnosis, prognosis, and delineation. However, gradient power and hardware limitations …
Hybrid no-reference quality metric for singly and multiply distorted images
In a typical image communication system, the visual signal presented to the end users may
undergo the steps of acquisition, compression and transmission which cause the artifacts of …
undergo the steps of acquisition, compression and transmission which cause the artifacts of …
TRQ3DNet: A 3D quasi-recurrent and transformer based network for hyperspectral image denoising
L Pang, W Gu, X Cao - Remote Sensing, 2022 - mdpi.com
We propose a new deep neural network termed TRQ3DNet which combines convolutional
neural network (CNN) and transformer for hyperspectral image (HSI) denoising. The network …
neural network (CNN) and transformer for hyperspectral image (HSI) denoising. The network …
Complexity classes in communication complexity theory
We take a complexity theoretic view of AC Yao's theory of communication complexity. A rich
structure of natural complexity classes is introduced. Besides providing a more structured …
structure of natural complexity classes is introduced. Besides providing a more structured …
No-reference image and video quality assessment: a classification and review of recent approaches
The field of perceptual quality assessment has gone through a wide range of developments
and it is still growing. In particular, the area of no-reference (NR) image and video quality …
and it is still growing. In particular, the area of no-reference (NR) image and video quality …
Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic …
S Roy, TD Whitehead, JD Quirk, A Salter… - …, 2020 - thelancet.com
Background Radiomics analyses has been proposed to interrogate the biology of tumour as
well as to predict/assess response to therapy in vivo. The objective of this work was to …
well as to predict/assess response to therapy in vivo. The objective of this work was to …
Exposing region splicing forgeries with blind local noise estimation
Region splicing is a simple and common digital image tampering operation, where a chosen
region from one image is composited into another image with the aim to modify the original …
region from one image is composited into another image with the aim to modify the original …