Fully-automatic inverse tone mapping algorithm based on dynamic mid-level tone mapping

High Dynamic Range (HDR) displays can show images with higher color contrast levels and peak luminosities than the common Low Dynamic Range (LDR) displays. However, most existing video content is recorded and/or graded in LDR format. To show LDR content on HDR displays, it needs to be up-scaled using a so-called inverse tone mapping algorithm. Several techniques for inverse tone mapping have been proposed in the last years, going from simple approaches based on global and local operators to more advanced algorithms such as neural networks. Some of the drawbacks of existing techniques for inverse tone mapping are the need for human intervention, the high computation time for more advanced algorithms, limited low peak brightness, and the lack of the preservation of the artistic intentions. In this paper, we propose a fully-automatic inverse tone mapping operator based on mid-level mapping capable of real-time video processing. Our proposed algorithm allows expanding LDR images into HDR images with peak brightness over 1000 nits, preserving the artistic intentions inherent to the HDR domain. We assessed our results using the full-reference objective quality metrics HDR-VDP-2.2 and DRIM, and carrying out a subjective pair-wise comparison experiment. We compared our results with those obtained with the most recent methods found in the literature. Experimental results demonstrate that our proposed method outperforms the current state-of-the-art of simple inverse tone mapping methods and its performance is similar to other more complex and time-consuming advanced techniques

Contrast enhancement and exposure correction using a structure-aware distribution fitting

Realce de contraste e correção de exposição são úteis em aplicações domésticas e técnicas, no segundo caso como uma etapa de pré-processamento para outras técnicas ou para ajudar a observação humana. Frequentemente, uma transformação localmente adaptativa é mais adequada para a tarefa do que uma transformação global. Por exemplo, objetos e regiões podem ter níveis de iluminação muito diferentes, fenômenos físicos podem comprometer o contraste em algumas regiões mas não em outras, ou pode ser desejável ter alta visibilidade de detalhes em todas as partes da imagem. Para esses casos, métodos de realce de imagem locais são preferíveis. Embora existam muitos métodos de realce de contraste e correção de exposição disponíveis na literatura, não há uma solução definitiva que forneça um resultado satisfatório em todas as situações, e novos métodos surgem a cada ano. Em especial, os métodos tradicionais baseados em equalização adaptativa de histograma sofrem dos efeitos checkerboard e staircase e de excesso de realce. Esta dissertação propõe um método para realce de contraste e correção de exposição em imagens chamado Structure-Aware Distribution Stretching (SADS). O método ajusta regionalmente à imagem um modelo paramétrico de distribuição de probabilidade, respeitando a estrutura da imagem e as bordas entre as regiões. Isso é feito usando versões regionais das expressões clássicas de estimativa dos parâmetros da distribuição, que são obtidas substituindo a mé- dia amostral presente nas expressões originais por um filtro de suavização que preserva as bordas. Após ajustar a distribuição, a função de distribuição acumulada (CDF) do modelo ajustado e a inversa da CDF da distribuição desejada são aplicadas. Uma heurística ciente de estrutura que detecta regiões suaves é proposta e usada para atenuar as transformações em regiões planas. SADS foi comparado a outros métodos da literatura usando métricas objetivas de avaliação de qualidade de imagem (IQA) sem referência e com referência completa nas tarefas de realce de contraste e correção de exposição simultâneos e na tarefa de defogging/dehazing. Os experimentos indicam um desempenho geral superior do SADS em relação aos métodos comparados para os conjuntos de imagens usados, de acordo com as métricas IQA adotadas.Contrast enhancement and exposure correction are useful in domestic and technical applications, the latter as a preprocessing step for other techniques or for aiding human observation. Often, a locally adaptive transformation is more suitable for the task than a global transformation. For example, objects and regions may have very different levels of illumination, physical phenomena may compromise the contrast at some regions but not at others, or it may be desired to have high visibility of details in all parts of the image. For such cases, local image enhancement methods are preferable. Although there are many contrast enhancement and exposure correction methods available in the literature, there is no definitive solution that provides a satisfactory result in all situations, and new methods emerge each year. In special, traditional adaptive histogram equalization-based methods suffer from checkerboard and staircase effects and from over enhancement. This dissertation proposes a method for contrast enhancement and exposure correction in images named Structure-Aware Distribution Stretching (SADS). The method fits a parametric model of probability distribution to the image regionally while respecting the image structure and edges between regions. This is done using regional versions of the classical expressions for estimating the parameters of the distribution, which are obtained by replacing the sample mean present in the original expressions by an edge-preserving smoothing filter. After fitting the distribution, the cumulative distribution function (CDF) of the adjusted model and the inverse of the CDF of the desired distribution are applied. A structure-aware heuristic to indicate smooth regions is proposed and used to attenuate the transformations in flat regions. SADS was compared with other methods from the literature using objective no-reference and full-reference image quality assessment (IQA) metrics in the tasks of simultaneous contrast enhancement and exposure correction and in the task of defogging/dehazing. The experiments indicate a superior overall performance of SADS with respect to the compared methods for the image sets used, according to the IQA metrics adopted

Surface compositional mapping by spectral ratioing of ERTS-1 MSS data in the Wind River Basin and Range, Wyoming

The author has identified the following significant results. ERTS data collected in August and October 1972 were processed on digital and special purpose analog recognition computers using ratio enhancement and pattern recognition. Ratios of band-averaged laboratory reflectances of some minerals and rock types known to be in the scene compared favorably with ratios derived from the data by ratio normalization procedures. A single ratio display and density slice of the visible channels of ERTS MSS data, Channel 5/Channel 4 (R5,4), separated the Triassic Chugwater formation (redbeds) from other formations present and may have enhanced iron oxide minerals present at the surface in abundance. Comparison of data sets collected over the same area at two different times of the year by digital processing indicated that spectral variation due to environmental factors was reduced by ratio processing

Enhancement of Retinal Fundus Images via Pixel Color Amplification

We propose a pixel color amplification theory and family of enhancement methods to facilitate segmentation tasks on retinal images. Our novel re-interpretation of the image distortion model underlying dehazing theory shows how three existing priors commonly used by the dehazing community and a novel fourth prior are related. We utilize the theory to develop a family of enhancement methods for retinal images, including novel methods for whole image brightening and darkening. We show a novel derivation of the Unsharp Masking algorithm. We evaluate the enhancement methods as a pre-processing step to a challenging multi-task segmentation problem and show large increases in performance on all tasks, with Dice score increases over a no-enhancement baseline by as much as 0.491. We provide evidence that our enhancement preprocessing is useful for unbalanced and difficult data. We show that the enhancements can perform class balancing by composing them together.Comment: Accepted to International Conference on Image Analysis and Recognition, ICIAR 2020 ; // Published at https://doi.org/10.1007/978-3-030-50516-5_26 ;// CODE, SLIDES, and an expanded/modified 20 page version https://github.com/adgaudio/ietk-re

CNN Injected Transformer for Image Exposure Correction

Capturing images with incorrect exposure settings fails to deliver a satisfactory visual experience. Only when the exposure is properly set, can the color and details of the images be appropriately preserved. Previous exposure correction methods based on convolutions often produce exposure deviation in images as a consequence of the restricted receptive field of convolutional kernels. This issue arises because convolutions are not capable of capturing long-range dependencies in images accurately. To overcome this challenge, we can apply the Transformer to address the exposure correction problem, leveraging its capability in modeling long-range dependencies to capture global representation. However, solely relying on the window-based Transformer leads to visually disturbing blocking artifacts due to the application of self-attention in small patches. In this paper, we propose a CNN Injected Transformer (CIT) to harness the individual strengths of CNN and Transformer simultaneously. Specifically, we construct the CIT by utilizing a window-based Transformer to exploit the long-range interactions among different regions in the entire image. Within each CIT block, we incorporate a channel attention block (CAB) and a half-instance normalization block (HINB) to assist the window-based self-attention to acquire the global statistics and refine local features. In addition to the hybrid architecture design for exposure correction, we apply a set of carefully formulated loss functions to improve the spatial coherence and rectify potential color deviations. Extensive experiments demonstrate that our image exposure correction method outperforms state-of-the-art approaches in terms of both quantitative and qualitative metrics