4results about How to "Keep the color" patented technology

The application discloses a fabric yellowing reagent and a fabric yellowing method, and relates to the technical field of daily chemical products. 5 The fabric yellowing reagent comprises 1-3% of histidine solution and a bacterial suspension with a bacterial concentration of 1*10 5 CFU / mL-9*10 The bacteria are a mixture of one or more of Staphylococcus epidermidis and Micrococcus luteus; the solvent of the histidine solution is normal saline containing 0.15-1% of trypsin peptone; and the solvent of the bacterial suspension is normal saline containing 0.1-0.2% of trypsin soybean peptone liquid culture medium. The fabric yellowing reagent can be used for simulating fabric yellowing, and has good operability, reproducibility and data reliability.

This application relates to the field of laundry detergent processing technology, specifically disclosing a color-protecting and color-preserving compound additive, its preparation method, and its application. The color-protecting and color-preserving compound additive is mainly composed of the following raw materials in parts by weight: 10-20 parts polyvinylpyrrolidone, 15-25 parts modified starch, 3-5 parts dihydrazine adipic acid, 15-25 parts sodium carbonate, 6-10 parts sodium carboxymethyl cellulose, 8-12 parts zeolite 4A, 8-12 parts sodium sulfate, and 11-15 parts water. The modified starch is obtained by first grafting starch with 3-chloro-2-hydroxypropyltrimethylammonium chloride and 1-chlorohexane-5-en-2-ol, and then polymerizing it with diacetone acrylamide. This color-protecting and color-preserving compound additive, with the addition of polyvinylpyrrolidone to the raw materials, synergistic effects with the modified starch, and cross-linking with dihydrazine adipic acid, exhibits high color-protecting and color-preserving effects, maintaining the original color of clothing, extending the service life of clothing, and meeting market demand.

The application discloses a low-illumination image enhancement method based on an improved Retinex algorithm, and applies to the technical field of image enhancement, and comprises the following steps: performing discrete wavelet decomposition on a low-illumination image to obtain low-frequency and high-frequency components of the image; converting the low-frequency component into HSV, separately performing brightness correction on a V channel, then converting back to RGB, performing bilateral filtering, and then converting to HSV to extract the V channel; performing image enhancement on the low-frequency component by using an improved Retinex algorithm based on a joint weighting of a bilateral filter and a Gaussian filter as a new center-surround function, and performing median filtering processing, then converting to HSV to extract the V channel; weighting and fusing the two V channels, retaining the H and S channels after algorithm enhancement, then converting back to RGB, performing discrete wavelet fusion with the high-frequency component after denoising, and stretching and outputting the enhanced low-illumination image. The application can effectively guarantee the color, edge details and avoid local distortion of the image.

The invention relates to the technical field of tobacco baking, and provides a baking process based on the quality of fresh tobacco leaves of Xiangyan No.7. The baking process comprises the following steps: S1, carrying out near infrared spectrum scanning on harvested fresh tobacco leaves to obtain near infrared spectrum data of each tobacco leaf; s2, dividing the near infrared spectrum data into a training set and a test set; s3, taking the near infrared spectrum data of each tobacco leaf in the training set as an input vector, inputting the input vector into a self-organizing neural network model for clustering, and dividing the tobacco leaves into three subsets; s4, aiming at the three divided subsets, baking process parameters are optimized according to a three-section baking process, and the three-section baking process comprises a yellowing period, a color fixing period and a stem drying period; and S5, performing near infrared spectrum scanning on newly harvested tobacco leaves, classifying the newly harvested tobacco leaves through the self-organizing neural network model, determining subsets to which the newly harvested tobacco leaves belong, and according to the baking process curves corresponding to the subsets, sequentially executing the baking processes of a yellowing period, a color fixing period and a stem drying period.