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Cloud storage represents the trend of intensive, scale and specialization of information technology, which has changed the technical architecture and implementation method of electronic records management. Moreover, it will provide a convenient way to generate more advanced and efficient management of the electronic data records. However, in cloud storage environment, it is difficult to guarantee the trustworthiness of electronic records, which results in a series of severe challenges to electronic records management. Starting from the definition and specification of electronic records, this paper firstly analyzes the requirements of the trustworthiness in cloud storage during their long-term preservation according to the information security theory and subdivides the trustworthiness into the authenticity, integrity, usability, and reliability of electronic records in cloud storage. Moreover, this paper proposes the technology framework of preservation for trusted electronic records. Also, the technology of blockchain, proofs of retrievability, the open archival information system model and erasure code are adopted to protect these four security attributes, to guarantee the credibility of the electronic record.

This paper proposes a novel method for image magnification by exploiting the property that the intensity of an image varies along the direction of the gradient very quickly. It aims to maintain sharp edges and clear details. The proposed method first calculates the gradient of the low-resolution image by fitting a surface with quadratic polynomial precision. Then, bicubic interpolation is used to obtain initial gradients of the high-resolution (HR) image. The initial gradients are readjusted to find the constrained gradients of the HR image, according to spatial correlations between gradients within a local window. To generate an HR image with high precision, a linear surface weighted by the projection length in the gradient direction is constructed. Each pixel in the HR image is determined by the linear surface. Experimental results demonstrate that our method visually improves the quality of the magnified image. It particularly avoids making jagged edges and bluring during magnification.

Part of important structural edges in the image is smoothed due to the small gradients, while the others are preserved with greater gradients. Therefore, the authors propose a two‐stage image smoothing method based on edge‐patch histogram equalisation and patch decomposition. The authors' purpose is to increase the gradient of important structural edges while reducing the gradient of the texture region. Therefore, they divide the image into edge‐patches where the structural edges are concentrated or non‐edge‐patches where the texture details are concentrated by image segmentation. The edge‐patch needs to be equalised by the histograms for increasing the gradient of the edge pixels. All patches are decomposed to extract the smooth component for reducing the gradient of pixels. The smooth component of each patch is smoothed via L0 gradient minimisation. In order to ensure the continuity of the patch boundaries, the edge‐patch is inversely equalised. Finally, the whole image is smoothed via L0 gradient minimisation for removing residual textures and seams. Experimental results demonstrate that the proposed method is more competitive in maintaining important structural edges and removing texture details than the state‐of‐the‐art approaches. The proposed method can be applied to many areas of image processing.

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