Natural Image Noise Dataset

Convolutional neural networks have been the focus of research aiming to solve image denoising problems, but their performance remains unsatisfactory for most applications. These networks are trained with synthetic noise distributions that do not accurately reflect the noise captured by image sensors. Some datasets of clean-noisy image pairs have been introduced but they are usually meant for benchmarking or specific applications. We introduce the Natural Image Noise Dataset (NIND), a dataset of DSLR-like images with varying levels of ISO noise which is large enough to train models for blind denoising over a wide range of noise. We demonstrate a denoising model trained with the NIND and show that it significantly outperforms BM3D on ISO noise from unseen images, even when generalizing to images from a different type of camera. The Natural Image Noise Dataset is published on Wikimedia Commons such that it remains open for curation and contributions. We expect that this dataset will prove useful for future image denoising applications.Comment: NTIRE at CVPR 201

Нечетко-логические методы в задаче детектирования границ объектов

Рассматривается задача уменьшения вычислительной сложности методов выделения контуров на изображениях. Решение поставленной задачи достигается модификацией детектора Канни двумя нечетко-логическими методами, позволяющими сократить число проходов по исходному изображению: в-первом случае, путем исключения двух проходов, связанных с определением наличия соседства претендующего на границу пикселя со смежными в рамке размером 3´3, а во-втором случае, исключением операции определения угла направления градиента путем формирования данной величины комбинацией нечетких правил. Целью работы является уменьшение времени детектирования границ объектов на фото- видео-изображениях, за счет уменьшения вычислительной сложности применяемых методов. Интеллектуализация процесса детектирования границ осуществляется частичным повтором вычислительных операций, используемых в детекторе Канни, с дальнейшей заменой наиболее сложных вычислительных процедур. В предлагаемых методах после определения величины градиента и угла его направления осуществляется фаззификация восьми входных переменных, в качестве которых используется разность градиентов между центральной и смежными ячейками в рамке размером 3´3. Затем строится база нечетких правил. В первом методе в зависимости от угла направления градиента используются четыре нечетких правила и исключается один проход. Во втором методе шестнадцать нечетких правил сами задают угол направления градиента, при этом исключается два прохода вдоль изображения. Разность градиентов между центральной ячейкой и смежными ячейками позволяет учитывать форму распределения градиента. Затем на основе метода центра тяжести осуществляется дефаззификация результирующей переменной. Дальнейшее использование нечетких a-срезов позволяет осуществить бинаризацию результирующего изображения с выделением на нем границ объектов. Для оценки вычислительной скорости работы предложенных нечетких методов детектирования границ в среде Microsoft Visual Studio было разработано программное обеспечение. Представленные экспериментальные результаты показали, что уровень шума зависит от величины a-среза и параметров меток трапециевидных функций принадлежности. Ограничением двух методов является использование кусочно-линейных функций принадлежности. Экспериментальные исследования работоспособности предложенных методов детектирования контуров показали, что время первого нечеткого метода на 18% быстрее по сравнению с детектором Канни и на 2 % по отношению ко второму нечеткому методу. Однако при визуальной оценке установлено, что второй нечеткий метод лучше определяет границы объектов

Análise comparativa de técnicas de remoção de ruído em imagens coloridas

Orientador : Luiz Eduardo Soares de OliveiraMonografia (especialização) - Universidade Federal do Paraná, Setor de Ciências Exatas, Curso de Especialização em Data Science & Big Data.Inclui referências : p. 8-9.Resumo : A remoção de ruídos em imagens é uma das tarefas de processamento de imagens que ganhou atenção com o surgimento de métodos baseados em aprendizado de máquina. Estes métodos possibilitam a restauração de imagens afetadas por diferentes tipos de ruído, muitos destes mais complexos do que os ruídos sintéticos tradicionalmente testados. Entretanto, estes métodos em geral necessitam de treinamento extensivo com bases de dados grandes cujo preparo não é simples e, em muitos casos, inviável. Este estudo compara o comportamento e o desempenho de dois destes métodos juntamente com algumas técnicas tradicionalmente aplicadas para a tarefa de remoção de ruídos em imagens coloridas. O objetivo desta comparação é entender quão bom é o desempenho e a viabilidade desta nova abordagem quando aplicada à diferentes tipos de ruído, visando também oferecer uma plataforma que facilite a realização de novos experimentos com outras possíveis técnicas.Abstract : Image noise removal is an image processing task that gained attention with the appearance of machine learning methods. Those methods allow restoring images containing different kinds of noise, some of them more complex than the synthetic noise traditionally tested

Solar Energy Forecast for Integration of Grid and Balancing Power Using Profound Learning

The rapid and unexpected advancements in solar photovoltaic (PV) technology pose a future challenge for power sector experts responsible for managing the distribution of electricity, given the technology’s direct reliance on atmospheric and weather conditions. Therefore, the development of reliable predictive models for short-term solar PV generation forecasts becomes critically important to maintain a stable power supply and ensure seamless grid operations. With the evolution of deep learning and its intricate models, its application in this domain offers a more efficient means of achieving precise forecasts. As a result, the proposed system undergoes the following stages: a) Collecting data from the Sky Images and Photovoltaic Power Generation Dataset (SKIPDD) hosted on a GitHub repository, which contains one-minute intervals of 64x64 sky images and concurrent PV power generation data. b) Enhancing the PV input data through processes such as geometric correction, ortho rectification, pan sharpening, block adjustment, and histogram equalization. c) Extracting PV-related features from these images using an Autoencoder. d) forecasting using integration of CNNbased Bi-LSTM. Experimental evaluation states that the proposed system (ACNN-BiLSTM) outperforms better on various measures (accuracy:0.95, MSE:0.08, MAE: 0.02)

Image Denoising: Invertible and General Denoising Frameworks

The widespread use of digital cameras has resulted in a massive number of images being taken every day. However, due to the limitations of sensors and environments such as light conditions, the images are usually contaminated by noise. Obtaining visually clean images are essential for the accuracy of downstream high-level vision tasks. Thus, denoising is a crucial preprocessing step. A fundamental challenge in image denoising is to restore recognizable frequencies in edge and fine-scaled texture regions. Traditional methods usually employ hand-crafted priors to enhance the restoration of these high frequency regions, which seem to be omitted in current deep learning models. We explored whether the clean gradients can be utilized in deep networks as a prior as well as how to incorporate this prior in the networks to boost recovery of missing or obscured picture elements. We present results showing that fusing the pre-denoised images' gradient in the shallow layer contributes to recovering better edges and textures. We also propose a regularization loss term to ensure that the reconstructed images' gradients are close to the clean gradients. Both techniques are indispensable for enhancing the restored image frequencies. We also studied how to make the network preserve input information for better restoration of the high-frequency details. According to the definition of mutual information, we presented that invertibility is indispensable for information losslessness. Then, we proposed the Invertible Restoring Autoencoder (IRAE) network, a multiscale invertible encoder-decoder network. The superiority of this network was verified on three different low-level tasks, image denoising, JPEG image decompression and image inpainting. IRAE showed a good direction to explore more invertible architectures for image restoration. We attempted to further reduce the model size of invertible restoration networks. Our intuition was to use the same learned parameters to encode the noisy images in the forward pass and reconstruct the clean images in the backward pass. However, existing invertible networks use the same distribution for both the input and output obtained in the reversed pass. For our noise removal purpose, the input is noisy, but the reversed output is clean, following two different distributions. It was challenging to design lightweight invertible architectures for denoising. We presented InvDN, converting the noisy input to a clean low-resolution image and a noisy latent representation. To address the challenge mentioned above, we replaced the noisy representation with a clean one random sampled from Gaussian during the reverse pass. InvDN achieved state-of-the-art on real image denoising with much fewer parameters and less run time than existing state-of-the-art models. In addition, InvDN could also generate new noisy images for data augmentation. We also rethought image denoising from a novel aspect and introduced a more general denoising framework. Our framework utilized invertible networks to learn a noisy image distribution, which could be considered as the joint distribution of clean content and noise. The noisy input was mapped to representations in the latent space. A novel disentanglement strategy was applied to the latent representations to obtain the representations for the clean content, which were passed to the reversed network to get the clean image. Since this concept was a novel attempt, we also explored different data augmentation and training strategies for this framework. The proposed FDN was trained and tested from simple to complex tasks on distribution-clear class-specific synthetic noisy datasets, more general remote sensing datasets, and real noisy datasets and achieved competitive results with fewer parameters and faster speed. This work contributed a novel perspective and potential direction to design low-level task models in the future

On the Importance of Denoising when Learning to Compress Images

Image noise is ubiquitous in photography. However, image noise is not compressible nor desirable, thus attempting to convey the noise in compressed image bitstreams yields sub-par results in both rate and distortion. We propose to explicitly learn the image denoising task when training a codec. Therefore, we leverage the Natural Image Noise Dataset, which offers a wide variety of scenes captured with various ISO numbers, leading to different noise levels, including insignificant ones. Given this training set, we supervise the codec with noisy-clean image pairs, and show that a single model trained based on a mixture of images with variable noise levels appears to yield best-in-class results with both noisy and clean images, achieving better rate-distortion than a compression-only model or even than a pair of denoising-then-compression models with almost one order of magnitude fewer GMac operations